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Article

Intelligent Tennis Robot Based on a Deep Neural Network

by 1,*,†, 1, 2 and 3
1
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
2
Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S10 2TN, UK
3
Center for Artificial Intelligence, University of Technology, Sydney 2007, Australia
*
Author to whom correspondence should be addressed.
Current address: 99 Shangda Road, Shanghai, China.
Appl. Sci. 2019, 9(18), 3746; https://doi.org/10.3390/app9183746
Received: 15 August 2019 / Accepted: 4 September 2019 / Published: 8 September 2019
(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)
In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%. View Full-Text
Keywords: object detection; deep neural network; YOLOv3; maximum clique problem; pointer network; backtracking algorithm object detection; deep neural network; YOLOv3; maximum clique problem; pointer network; backtracking algorithm
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MDPI and ACS Style

Gu, S.; Zeng, W.; Jia, Y.; Yan, Z. Intelligent Tennis Robot Based on a Deep Neural Network. Appl. Sci. 2019, 9, 3746. https://doi.org/10.3390/app9183746

AMA Style

Gu S, Zeng W, Jia Y, Yan Z. Intelligent Tennis Robot Based on a Deep Neural Network. Applied Sciences. 2019; 9(18):3746. https://doi.org/10.3390/app9183746

Chicago/Turabian Style

Gu, Shenshen, Wei Zeng, Yuxuan Jia, and Zheng Yan. 2019. "Intelligent Tennis Robot Based on a Deep Neural Network" Applied Sciences 9, no. 18: 3746. https://doi.org/10.3390/app9183746

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